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This research was triggered by the identified need in literature for large-scale studies about the kinds of designs that teachers create for mobile learning (m-learning). These studies require analyses of large datasets of learning designs. The common approach followed by researchers when analyzing designs has been to manually classify them following high-level pedagogically guided coding strategies, which demands extensive work. Therefore, the first goal of this paper is to explore the use of supervised machine learning (SML) to automatically classify the textual content of m-learning designs using pedagogically relevant classifications, such as the cognitive level demanded by students to carry out specific designed tasks, the phases of inquiry learning represented in the designs, or the role that the situated environment has in the designs. Because not all SML models are transparent, but researchers often need to understand their behaviour, the second goal of this paper is to consider the trade-off between models' performance and interpretability in the context of design analytics for m-learning. To achieve these goals, we compiled a dataset of designs deployed using two tools, Avastusrada and Smartzoos. With this dataset, we trained and compared different models and feature extraction techniques. We further optimized and compared the best performing and most interpretable algorithms (EstBERT and Logistic Regression) to consider the second goal with an illustrative case. We found that SML can reliably classify designs with accuracy > 0.86 and Cohen's kappa > 0.69.

Research proved that anxiety plays an essential role in foreign language speaking, especially when it comes to speaking in front of others. Learners in the department of English always show a negative attitude in speaking presentations and tasks in full view of people. The aim of this paper is to investigate the factors that contribute to learners' anxiety in foreign language speaking from learners' and teachers' perspectives. The study aims to answer the following questions: what are the factors that cause learners' speaking anxiety and what types of anxiety do students have, and what are some strategies to lower it? The study was conducted through mixed method research to achieve the purposes of both qualitative and quantitative methods. The subjects of the study were 240 students and ten teachers in the foundation program. These students are studying at different levels from 1 to 4 to be prepared for the academic programs, which are business, IT and engineering. The research data was collected through a questionnaire for learners and an interview with teachers. The questionnaire is based mainly on Horwitz's scale, which is always considered the best scale to measure anxiety and anxiety indicators. The data revealed that most of the students face all types of anxiety in speaking, including communication apprehension, fear of negative evaluation and speaking test anxiety. From the teachers' perspective, anxiety was also related to the syllabus itself and the types of tasks given. To solve the issue of anxiety, the self-system model of motivation was implemented with a group of students giving them speaking tasks using the motivational model of Hadfield and Dornyei (2014), where we adopted some tasks to make a unit of teaching relying on Willis' Task-based framework where we added the language focus to each task.

Scrum is a widely-used framework in industry, so many schools apply it to their software engineering courses, particularly capstone courses. Due to the differences between students and industrial professionals, changing Scrum is necessary to fit capstone projects. In this paper, we suggest a decision-making process to assist instructors in developing a strategy to adapt Scrum for their course. This framework considers critical differences, such as student's workloads and course schedules, and keeps the Agile principles and Scrum events. To evaluate the adapted Scrum, we investigated student's learning experiences, satisfaction, and performance by quantitatively analyzing user story points and source codes and qualitatively studying instructor's evaluations, student's feedback, and Sprint Retrospective notes. Our two case studies about adapted Scrum showed that having daily stand-up meetings in every class was not helpful, student's satisfaction positively correlated to the difficulty of the task they tackled, and the project provided good learning experiences.

Contribution: This article features a systematic literature review with the objective of presenting a study that reflects the current scenario of research on the cognitive load theory (CLT) in the domain of teaching and learning computer programming. Background: Computer programming is a highly cognitive skill, requiring mastering multiple competencies, and recognized as being difficult to learn, for this reason, the cognitive load (CL) in the learner’s working memory emerged as an influential concept, making CLT one of the most common theories in computing education research. Research Questions: What are the approaches that relate CLT to teaching and learning computer programming? What CLT-related concepts are covered? What evidence is reported with respect to this context? Methodology: Following a formal protocol, a survey was performed for papers linking CLT to teaching and learning programming published between 2010 and 2020. The selection of papers was based on a set of criteria established to drive the selection process, including alignment with the research questions and paper quality assessment. Findings: The approaches applied in the papers are based on measuring the CL; through instructional design based on the development or use of resources or tools, a range of different pedagogy strategies and the CLT concepts. With respect to the concepts, the subjective measurement technique and worked example effect are the most commonly deployed. As far as the evidence reported, the better part is related to the worked example effect and measuring CLs. [ABSTRACT FROM AUTHOR]

The reform of the European academic landscape with the introduction of bachelor's and master's degree programs has brought about several profound changes for teaching and assessment in higher education. With regard to the examination system, the shift towards output-oriented teaching is still one of the most significant challenges. Assessments have to be integrated into the teaching and learning arrangements and consistently aligned towards the intended learning outcomes. In particular, assessments should provide valid evidence that learners have acquired competences that are relevant for a specific domain. However, it seems that this didactic goal has not yet been fully achieved in modeling education in computer science. The aim of this study is to investigate whether typical task material used in exercises and exams in modeling education at selected German universities covers relevant competences required for graphical modeling. For this purpose, typical tasks in the field of modeling are first identified by means of a content-analytical procedure. Subsequently, it is determined which competence facets relevant for graphical modeling are addressed by the task types. By contrasting a competence model for modeling with the competences addressed by the tasks, a gap was identified between the required competences and the task material analyzed. In particular, the gap analysis shows the neglect of transversal competence facets as well as those related to the analysis and evaluation of models. The result of this paper is a classification of task types for modeling education and a specification of the competence facets addressed by these tasks. Recommendations for developing and assessing student's competences comprehensively are given. [ABSTRACT FROM AUTHOR]

The reform of the European academic landscape with the introduction of bachelor’s and master’s degree programs has brought about several profound changes for teaching and assessment in higher education. With regard to the examination system, the shift towards output-oriented teaching is still one of the most significant challenges. Assessments have to be integrated into the teaching and learning arrangements and consistently aligned towards the intended learning outcomes. In particular, assessments should provide valid evidence that learners have acquired competences that are relevant for a specific domain. However, it seems that this didactic goal has not yet been fully achieved in modeling education in computer science. The aim of this study is to investigate whether typical task material used in exercises and exams in modeling education at selected German universities covers relevant competences required for graphical modeling. For this purpose, typical tasks in the field of modeling are first identified by means of a content-analytical procedure. Subsequently, it is determined which competence facets relevant for graphical modeling are addressed by the task types. By contrasting a competence model for modeling with the competences addressed by the tasks, a gap was identified between the required competences and the task material analyzed. In particular, the gap analysis shows the neglect of transversal competence facets as well as those related to the analysis and evaluation of models. The result of this paper is a classification of task types for modeling education and a specification of the competence facets addressed by these tasks. Recommendations for developing and assessing student’s competences comprehensively are given. [ABSTRACT FROM AUTHOR]